def table(self, data=None):
"""Display a static table.
This differs from `st.dataframe` in that the table in this case is
static: its entire contents are laid out directly on the page.
Parameters
----------
data : pandas.DataFrame, pandas.Styler, numpy.ndarray, Iterable, dict,
or None
The table data.
Example
-------
>>> df = pd.DataFrame(
... np.random.randn(10, 5),
... columns=('col %d' % i for i in range(5)))
...
>>> st.table(df)
.. output::
https://static.streamlit.io/0.25.0-2JkNY/index.html?id=KfZvDMprL4JFKXbpjD3fpq
height: 480px
"""
table_proto = DataFrameProto()
marshall_data_frame(data, table_proto)
return self.dg._enqueue("table", table_proto)
def dataframe(self, data=None, width=None, height=None):
"""Display a dataframe as an interactive table.
Parameters
----------
data : pandas.DataFrame, pandas.Styler, numpy.ndarray, Iterable, dict,
or None
The data to display.
If 'data' is a pandas.Styler, it will be used to style its
underyling DataFrame. Streamlit supports custom cell
values and colors. (It does not support some of the more exotic
pandas styling features, like bar charts, hovering, and captions.)
Styler support is experimental!
width : int or None
Desired width of the UI element expressed in pixels. If None, a
default width based on the page width is used.
height : int or None
Desired height of the UI element expressed in pixels. If None, a
default height is used.
Examples
--------
>>> df = pd.DataFrame(
... np.random.randn(50, 20),
... columns=('col %d' % i for i in range(20)))
...
>>> st.dataframe(df) # Same as st.write(df)
.. output::
https://static.streamlit.io/0.25.0-2JkNY/index.html?id=165mJbzWdAC8Duf8a4tjyQ
height: 330px
>>> st.dataframe(df, 200, 100)
You can also pass a Pandas Styler object to change the style of
the rendered DataFrame:
>>> df = pd.DataFrame(
... np.random.randn(10, 20),
... columns=('col %d' % i for i in range(20)))
...
>>> st.dataframe(df.style.highlight_max(axis=0))
.. output::
https://static.streamlit.io/0.29.0-dV1Y/index.html?id=Hb6UymSNuZDzojUNybzPby
height: 285px
"""
data_frame_proto = DataFrameProto()
marshall_data_frame(data, data_frame_proto)
return self.dg._enqueue(
"data_frame",
data_frame_proto,
element_width=width,
element_height=height,
)